Analytics (Objective 1) 1) Case Prioritization and Eligibility The objective of this focus area is to automate the collection and analysis of applicant data to improve the efficiency of SSA processes that rank cases based upon adjudication complexity, types of medical allegations, and other criteria of interest. Compassionate Allowance (CAL) case identification: One way that the SSA prioritizes its cases is to expedite decisions for those applicants who have a diagnosis that suggests that they have a high likelihood of allowance. SSA launched the CAL program in 2008, which was the latest of several expedited award initiatives intended to fast-track applicants who clearly met SSAs definition of disability. Currently, SSA uses software to automatically select cases for the CAL program. The aim of this subproject is to improve the precision of this software using various data science methods. Last year, we provided SSA with recommendations to improve performance of the CAL software for thirteen CAL conditions. This year, we planned to provide revisions to these recommendations based on SSA feedback. Having received no feedback to date, no revisions have been necessary. 2) Adjudicator support tools Initial SSA decisions are made by examiners who have very little time to review evidence for a case, usually in the form of lengthy medical records from various healthcare providers. The objective of this focus area is to develop, adapt, and apply methods that aid SSA disability adjudicators to reach accurate, consistent, and timely decisions in accordance with SSA regulations and available medical evidence. This year, NIH deliverables encompassed the identification of functional terminology, specifically through a preliminary demonstration of named entity recognition and a demonstration of document classification. Identification of functional terminology: The aim of this subproject is to extract functional information from clinical documentation, which is an underdeveloped area of machine learning and natural language processing (NLP). In order to identify functional information, it is necessary to understand the variation in text used in clinical documentation, noting those rich in functional information compared to those focusing on health conditions. This year, we made progress on methods to characterize medical documentation with respect to functional language and extracting this information from samples of medical documents. To advance this work, we continued to develop annotation resources developed by content experts to serve as a gold standard for machine learning methods. We provided a demonstration of specific methods in NLP, including named entity recognition and document classification, and developed a preliminary functional language terminology for the mobility domain, a necessary foundation to ontology development. Automating the extraction, retrieval, and classification of functional information is intended to improve the efficiency of SSAs processes. WD-FAB development (Objective 2) In collaboration with the SSA, the NIH and Boston University developed a comprehensive and efficient assessment instrument called the Work Disability Functional Assessment Battery (WD-FAB). Contemporary models of disability indicate that in order to assess work disability, what individuals can do and what they are expected to do for work must both be assessed. The WD-FAB is intended to assess what individuals can do. The WD-FAB is a 15-20-minute individualized assessment of functional activity that uses Item Response Theory (IRT), along with computer adaptive technology (CAT), to select the most relevant test items from a large pool of items to measure self-reported functional ability. Item-based scoring means respondents do not need to answer all items or the same items to obtain comparative scores and scores are obtained in a highly efficient manner. This year, development of the WD-FAB has focused on implementing optimal methods in item response theory into the WD-FAB software. In addition, SSA commissioned the design of a preliminary pilot study examining the WD-FAB as applied to the continuing disability review (CDR) process to inform considerations for a future large scale study and potential implementation in the CDR process. 3) Functional Assessment Tools The objective of this focus area is to develop new ways to collect, structure, and interpret functional data for use by SSA. This work will include development of the WD-FAB and methods to assist in interpreting WD-FAB results. WD-FAB instrument development: The aim of this subproject is to finalize the development of the WD-FAB so that it is ready for real-world, applied testing. The instrument now includes over 300 items across eight domains, four of which represent physical function (basic mobility, upper body function, fine motor function, community mobility) and four of which represent mental health function (communication & cognition, resilience & sociability, self-regulation, and mood & emotions). Functional stages (e.g., low, moderate, high functioning) were developed by content experts to aid score interpretation. To date, the reliability and validity of the WD-FAB have been supported by a variety of evidence from a continuum of studies. This year, we provided updated WD-FAB software to SSA and conducted additional simulation studies to assess the revised instrument. Continuing Disability Review study design: Once an individual is awarded disability benefits, their disability status is reassessed periodically. Following development of the WD-FAB, SSA requested creation of a preliminary pilot study, as well as a large scale study, to examine the utility of the WD-FAB in the SSA continuing disability review process. These study designs were delivered to SSA this year. In addition, we developed an additional study design aimed at enhancing the robustness of the WD-FAB. SSA will subsequently determine whether and when to implement these studies. Abstract Morris Z, Chin LMK, Chan L & Keyser RE (2019). Ventilatory efficiency among patients with pulmonary hypertension with varying level of adaptation to exercise training. Med Sci Sports Exerc 51(5):S335. Presented at the 2019 annual meeting of the American College of Sports Medicine in Orlando, Florida.